Int J Health Geogr - Creating 'obesogenic realities'; do our methodological choices make a difference when measuring the food environment?


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CKGROUND: The use of Geographical Information Systems (GIS) to objectively measure 'obesogenic' food environment (foodscape) exposure has become common-place. This increase in usage has coincided with the development of a methodologically heterogeneous evidence-base, with subsequent perceived difficulties for inter-study comparability. However, when used together in previous work, different types of food environment metric have often demonstrated some degree of covariance. Differences and similarities between density and proximity metrics, and within methodologically different conceptions of density and proximity metrics need to be better understood.METHODS: Frequently used measures of food access were calculated for North East England, UK. Using food outlet data from local councils, densities of food outlets per 1000 population and per km? were calculated for small administrative areas. Densities (counts) were also calculated based on population-weighted centroids of administrative areas buffered at 400/800/1000 m street network and Euclidean distances. Proximity (street network and Euclidean distances) from these centroids to the nearest food outlet were also calculated. Metrics were compared using Spearman's rank correlations.RESULTS: Measures of foodscape density and proximity were highly correlated. Densities per km? and per 1000 population were highly correlated (r(s)=0.831). Euclidean and street network based measures of proximity (r(s)=0.865) and density (r(s)=0.667-0.764, depending on neighbourhood size) were also highly correlated. Density metrics based on administrative areas and buffered centroids of administrative areas were less strongly correlated (r(s)=0.299-0.658).CONCLUSIONS: Density and proximity metrics were largely comparable, with some exceptions. Whilst results suggested a substantial degree of comparability across existing studies, future comparability could be ensured by moving towards a more standardised set of environmental metrics, where appropriate, lessening the potential pitfalls of methodological variation between studies. The researchers' role in creating their own obesogenic 'reality' should be better understood and acknowledged.

Resumo Limpo

ckground use geograph inform system gis object measur obesogen food environ foodscap exposur becom commonplac increas usag coincid develop methodolog heterogen evidencebas subsequ perceiv difficulti interstudi compar howev use togeth previous work differ type food environ metric often demonstr degre covari differ similar densiti proxim metric within methodolog differ concept densiti proxim metric need better understoodmethod frequent use measur food access calcul north east england uk use food outlet data local council densiti food outlet per popul per km calcul small administr area densiti count also calcul base populationweight centroid administr area buffer m street network euclidean distanc proxim street network euclidean distanc centroid nearest food outlet also calcul metric compar use spearman rank correlationsresult measur foodscap densiti proxim high correl densiti per km per popul high correl rs euclidean street network base measur proxim rs densiti rs depend neighbourhood size also high correl densiti metric base administr area buffer centroid administr area less strong correl rsconclus densiti proxim metric larg compar except whilst result suggest substanti degre compar across exist studi futur compar ensur move toward standardis set environment metric appropri lessen potenti pitfal methodolog variat studi research role creat obesogen realiti better understood acknowledg

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